How to Describe Natural Language Processing in Artificial Intelligence

Artificial intelligence is a way of making a computer, a computer-controlled robot, or software to think intelligently and act intelligently, in a similar manner, the intelligent human brain thinks.

Goals of Artificial Intelligence

Create Expert Systems: The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advise its users on its own.

Implement Human Intelligence in Machines: Creating systems or robots that understand, think, learn, and behave like humans, even more accurately.

Applications of Artificial Intelligence

AI has been used in various fields, such as:

Gaming

Strategic games such as chess, poker, tic-tac-toe, etc., where a computer can think of a large number of possible positions based on historical recorded knowledge (game playing steps).

Natural Language Processing

Make computers more interactable with users that system understands natural language spoken by humans.

Expert Systems

Expert systems like smart applications that integrate machines, software, which is developed to solve complex problems in a particular domain, at the level of extraordinary human intelligence and expertise.

Vision Systems

These systems are capable of understanding, interpreting, and comprehending visual input on the computer. For example, the systems are used in Aeroplane to spy pieces of information from the areas, a clinical expert system used for diagnosis.

Speech Recognition

Makes intelligent systems that are capable of hearing and comprehending the language in terms of sentences and their meanings with human verbal interaction.

Handwriting Recognition

AI used in the handwriting recognition software reads the text written on paper by a pen or on-screen by a stylus. It can convert the human-written text into a readable test in the system.

Intelligent Robots

Robots can perform the tasks assigned by humans. Comprise of different sensors (such as light, heat, temperature, movement, sound, bump, and pressure). Robots have powerful processors, multiple sensors, and large size memory, to exhibit intelligence. In addition, they learn from their mistakes, and they can adapt to the new environment. This is like next-generation robots, which can be replaced with manpower into the industries.

Let’s Talk about NLP (Natural Language Processing) in Details

The artificial intelligence field that focuses on the interactions between human language and computers is called Natural Language Processing.

NLP is a method by which computers analyze, understand, and derive meaning from human language in a smart and useful way. By using NLP algorithms, developers can structure the algorithm to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.

Applications of Natural Language Processing

Summarizer is used to extract the most important and central ideas while ignoring irrelevant information from the Summarize blocks of text.

To Create a chatbot use Parsey McParseface, a language parsing deep learning model developed by Google that uses Point-of-Speech tagging.

Automatically generate keyword tags from content using AutoTag, which leverages LDA, a technique that discovers topics contained within a body of the text.

Identify the type of entity extracted, such as it is a person, place, or organization using Named Entity Recognition.

Use Sentiment Analysis to identify the sentiment of a string of text, from very negative reviews to neutral reviews to very positive reviews.

Also, PorterStemmer uses to reduce words to their root, or stem, or break up text into tokens using Tokenizer.

There are two components of NLP as given:

Natural Language Understanding (NLU)

Uses of Natural Language Understanding (NLU) are as follows:

NLU is used to Mapping the given input in natural language into useful representations.

Also, NLU Analyzing different aspects of the language.

Natural Language Generation (NLG)

Natural Language Generation is the process of producing meaningful phrases and sentences in the form of natural language (such as English) from some internal representation algorithm.

Application of Natural Language Generation (NLG)

Text planning: It includes retrieving the relevant content from the knowledge base.

Text Realization: It is used to mapping the sentence plan into sentence structure. The NLU is harder than NLG.

Benefits of NLP

NLP improved the accuracy and efficiency of documentation.

NLP automatically makes a readable summary text.

Useful for personal assistants such as Alexa.

Also, easier to perform sentiment analysis.

Here are some of the Open source NLP tools are as follows:

Stanford’s Core NLP Suite: A GPL-licensed framework of tools for processing English, Chinese, and Spanish, and it includes tools for tokenization, part of speech tagging, grammar parsing, named entity recognition, and more.

Natural Language Toolkit: It includes capabilities for tokenizing, parsing, and identifying named entities as well as many more features in it.

Apache Lucene and Solr: Advanced string manipulation utilities to powerful and flexible tokenization libraries to blazing-fast libraries for working with finite state automatons.

Apache OpenNLP: Apache-licensed suite of tools to do tasks like tokenization, part of speech tagging, parsing, and named entity recognition.

As you know, there are many open-source Natural Language Processing (NLP) libraries; some are the following:

Natural Language Toolkit (NLTK)

TextBlob

CoreNLP

Gensim

spaCy

polyglot

scikit–learn

Pattern

Conclusion

Hence, NLP is one of the components of artificial intelligence, which is the ability of a computer program to understand human speech.
Generally, developing NLP applications can be a tough task due to the precise requirements, but the presence of different NLP libraries makes everything possible easier. Therefore, NLP is contributing a great role in artificial intelligence.

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I am a Data Analyst with Loginworks Softwares. My expertise is Data Visualization and Analysis. I have been working with Loginworks for 1 year. I believe in sharing my technical knowledge with others. I believe that "one should always keep learning."